It is desirable to automatically and accurately determine the direction of a moving object, such as a car or other vehicle traveling on a road or highway, based on inputs provided by sensors along the object's path.
Currently known methods and systems may include the use of a truth table to determine a moving object's direction as it triggers sensors along its path. Truth tables use a pre-populated list of input state sequences paired with the corresponding direction of the moving object. A disadvantage to truth tables is that if a sequence is not found in the truth table, a direction cannot be determined.
Other currently known methods and systems implement the use of neural networks. Neural networks are very sophisticated modeling techniques capable of modeling extremely complex functions. Neural networks learn by example as opposed to having the logic established by a user. Although these systems may determine a moving object's direction with a high degree of accuracy, they are very expensive and complex to reconfigure at will.
There is, therefore, an increasing but unmet demand for methods and systems to determine the direction of a moving object, such as a car or other vehicle on a roadway, based on inputs provided by sensors along the object's path that result in a high degree of accuracy, but are simple to reconfigure and are more economical than other currently known solutions.